Matthew Shardlow


2023

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BioLaySumm 2023 Shared Task: Lay Summarisation of Biomedical Research Articles
Tomas Goldsack | Zheheng Luo | Qianqian Xie | Carolina Scarton | Matthew Shardlow | Sophia Ananiadou | Chenghua Lin
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023. The goal of this shared task is to develop abstractive summarisation models capable of generating “lay summaries” (i.e., summaries that are comprehensible to non-technical audiences) in both a controllable and non-controllable setting.There are two subtasks: 1) Lay Summarisation, where the goal is for participants to build models for lay summary generation only, given the full article text and the corresponding abstract as input; and2) Readability-controlled Summarisation, where the goal is for participants to train models to generate both the technical abstract and the lay summary, given an article’s main text as input.In addition to overall results, we report on the setup and insights from the BioLaySumm shared task, which attracted a total of 20 participating teams across both subtasks.

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ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval
Kai North | Alphaeus Dmonte | Tharindu Ranasinghe | Matthew Shardlow | Marcos Zampieri
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

Lexical simplification (LS) automatically replaces words that are deemed difficult to understand for a given target population with simpler alternatives, whilst preserving the meaning of the original sentence. The TSAR-2022 shared task on LS provided participants with a multilingual lexical simplification test set. It contained nearly 1,200 complex words in English, Portuguese, and Spanish and presented multiple candidate substitutions for each complex word. The competition did not make training data available; therefore, teams had to use either off-the-shelf pre-trained large language models (LLMs) or out-domain data to develop their LS systems. As such, participants were unable to fully explore the capabilities of LLMs by re-training and/or fine-tuning them on in-domain data. To address this important limitation, we present ALEXSIS+, a multilingual dataset in the aforementioned three languages, and ALEXSIS++, an English monolingual dataset that together contains more than 50,000 unique sentences retrieved from news corpora and annotated with cosine similarities to the original complex word and sentence. Using these additional contexts, we are able to generate new high-quality candidate substitutions that improve LS performance on the TSAR-2022 test set regardless of the language or model.

2022

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Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
Sanja Štajner | Horacio Saggion | Daniel Ferrés | Matthew Shardlow | Kim Cheng Sheang | Kai North | Marcos Zampieri | Wei Xu
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

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An Investigation into the Effect of Control Tokens on Text Simplification
Zihao Li | Matthew Shardlow | Saeed Hassan
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

Recent work on text simplification has focused on the use of control tokens to further the state of the art. However, it is not easy to further improve without an in-depth comprehension of the mechanisms underlying control tokens. One unexplored factor is the tokenisation strategy, which we also explore. In this paper, we (1) reimplemented ACCESS, (2) explored the effects of varying control tokens, (3) tested the influences of different tokenisation strategies, and (4) demonstrated how separate control tokens affect performance. We show variations of performance in the four control tokens separately. We also uncover how the design of control tokens could influence the performance and propose some suggestions for designing control tokens, which also reaches into other controllable text generation tasks.

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UoM&MMU at TSAR-2022 Shared Task: Prompt Learning for Lexical Simplification
Laura Vásquez-Rodríguez | Nhung Nguyen | Matthew Shardlow | Sophia Ananiadou
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

We present PromptLS, a method for fine-tuning large pre-trained Language Models (LM) to perform the task of Lexical Simplification. We use a predefined template to attain appropriate replacements for a term, and fine-tune a LM using this template on language specific datasets. We filter candidate lists in post-processing to improve accuracy. We demonstrate that our model can work in a) a zero shot setting (where we only require a pre-trained LM), b) a fine-tuned setting (where language-specific data is required), and c) a multilingual setting (where the model is pre-trained across multiple languages and fine-tuned in an specific language). Experimental results show that, although the zero-shot setting is competitive, its performance is still far from the fine-tuned setting. Also, the multilingual is unsurprisingly worse than the fine-tuned model. Among all TSAR-2022 Shared Task participants, our team was ranked second in Spanish and third in English.

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Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification
Horacio Saggion | Sanja Štajner | Daniel Ferrés | Kim Cheng Sheang | Matthew Shardlow | Kai North | Marcos Zampieri
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)

We report findings of the TSAR-2022 shared task on multilingual lexical simplification, organized as part of the Workshop on Text Simplification, Accessibility, and Readability TSAR-2022 held in conjunction with EMNLP 2022. The task called the Natural Language Processing research community to contribute with methods to advance the state of the art in multilingual lexical simplification for English, Portuguese, and Spanish. A total of 14 teams submitted the results of their lexical simplification systems for the provided test data. Results of the shared task indicate new benchmarks in Lexical Simplification with English lexical simplification quantitative results noticeably higher than those obtained for Spanish and (Brazilian) Portuguese.

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Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences
Piotr Przybyła | Matthew Shardlow
Findings of the Association for Computational Linguistics: ACL 2022

The environmental costs of research are progressively important to the NLP community and their associated challenges are increasingly debated. In this work, we analyse the carbon cost (measured as CO2-equivalent) associated with journeys made by researchers attending in-person NLP conferences. We obtain the necessary data by text-mining all publications from the ACL anthology available at the time of the study (n=60,572) and extracting information about an author’s affiliation, including their address. This allows us to estimate the corresponding carbon cost and compare it to previously known values for training large models. Further, we look at the benefits of in-person conferences by demonstrating that they can increase participation diversity by encouraging attendance from the region surrounding the host country. We show how the trade-off between carbon cost and diversity of an event depends on its location and type. Our aim is to foster further discussion on the best way to address the joint issue of emissions and diversity in the future.

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Towards Readability-Controlled Machine Translation of COVID-19 Texts
Fernando Alva-Manchego | Matthew Shardlow
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This project investigates the capabilities of Machine Translation models for generating translations at varying levels of readability, focusing on texts related to COVID-19. Whilst it is possible to automatically translate this information, the resulting text may contain specialised terminology, or may be written in a style that is difficult for lay readers to understand. So far, we have collected a new dataset with manual simplifications for English and Spanish sentences in the TICO-19 dataset, as well as implemented baseline pipelines combining Machine Translation and Text Simplification models.

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Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts
Matthew Shardlow | Fernando Alva-Manchego
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers. While Machine Translation technologies contribute to mitigate the first issue, the translated content will most likely still contain complex language. In order to investigate and address both problems simultaneously, we introduce Simple TICO-19, a new language resource containing manual simplifications of the English and Spanish portions of the TICO-19 corpus for Machine Translation of COVID-19 literature. We provide an in-depth description of the annotation process, which entailed designing an annotation manual and employing four annotators (two native English speakers and two native Spanish speakers) who simplified over 6,000 sentences from the English and Spanish portions of the TICO-19 corpus. We report several statistics on the new dataset, focusing on analysing the improvements in readability from the original texts to their simplified versions. In addition, we propose baseline methodologies for automatically generating the simplifications, translations and joint translation and simplifications contained in our dataset.

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An Evaluation of Binary Comparative Lexical Complexity Models
Kai North | Marcos Zampieri | Matthew Shardlow
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Identifying complex words in texts is an important first step in text simplification (TS) systems. In this paper, we investigate the performance of binary comparative Lexical Complexity Prediction (LCP) models applied to a popular benchmark dataset — the CompLex 2.0 dataset used in SemEval-2021 Task 1. With the data from CompLex 2.0, we create a new dataset contain 1,940 sentences referred to as CompLex-BC. Using CompLex-BC, we train multiple models to differentiate which of two target words is more or less complex in the same sentence. A linear SVM model achieved the best performance in our experiments with an F1-score of 0.86.

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Agree to Disagree: Exploring Subjectivity in Lexical Complexity
Matthew Shardlow
Proceedings of the 2nd Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI) within the 13th Language Resources and Evaluation Conference

Subjective factors affect our familiarity with different words. Our education, mother tongue, dialect or social group all contribute to the words we know and understand. When asking people to mark words they understand some words are unanimously agreed to be complex, whereas other annotators universally disagree on the complexity of other words. In this work, we seek to expose this phenomenon and investigate the factors affecting whether a word is likely to be subjective, or not. We investigate two recent word complexity datasets from shared tasks. We demonstrate that subjectivity is present and describable in both datasets. Further we show results of modelling and predicting the subjectivity of the complexity annotations in the most recent dataset, attaining an F1-score of 0.714.

2021

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Investigating Text Simplification Evaluation
Laura Vásquez-Rodríguez | Matthew Shardlow | Piotr Przybyła | Sophia Ananiadou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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SemEval-2021 Task 1: Lexical Complexity Prediction
Matthew Shardlow | Richard Evans | Gustavo Henrique Paetzold | Marcos Zampieri
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.

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Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification
Robert Flynn | Matthew Shardlow
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres. Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.

2020

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Multi-Word Lexical Simplification
Piotr Przybyła | Matthew Shardlow
Proceedings of the 28th International Conference on Computational Linguistics

In this work we propose the task of multi-word lexical simplification, in which a sentence in natural language is made easier to understand by replacing its fragment with a simpler alternative, both of which can consist of many words. In order to explore this new direction, we contribute a corpus (MWLS1), including 1462 sentences in English from various sources with 7059 simplifications provided by human annotators. We also propose an automatic solution (Plainifier) based on a purpose-trained neural language model and evaluate its performance, comparing to human and resource-based baselines.

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CompLex — A New Corpus for Lexical Complexity Prediction from Likert Scale Data
Matthew Shardlow | Michael Cooper | Marcos Zampieri
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)

Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such astext simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studieshave approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) fora set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using abinary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexityprediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl,and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.

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Detecting Multiword Expression Type Helps Lexical Complexity Assessment
Ekaterina Kochmar | Sian Gooding | Matthew Shardlow
Proceedings of the Twelfth Language Resources and Evaluation Conference

Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. Multiple NLP applications have been shown to benefit from MWE identification, however the research on lexical complexity of MWEs is still an under-explored area. In this work, we re-annotate the Complex Word Identification Shared Task 2018 dataset of Yimam et al. (2017), which provides complexity scores for a range of lexemes, with the types of MWEs. We release the MWE-annotated dataset with this paper, and we believe this dataset represents a valuable resource for the text simplification community. In addition, we investigate which types of expressions are most problematic for native and non-native readers. Finally, we show that a lexical complexity assessment system benefits from the information about MWE types.

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CombiNMT: An Exploration into Neural Text Simplification Models
Michael Cooper | Matthew Shardlow
Proceedings of the Twelfth Language Resources and Evaluation Conference

This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original paper. Alongside the replication results, we present our improvements dubbed CombiNMT. By using an updated implementation of OpenNMT, and incorporating the Newsela corpus alongside the original Wikipedia dataset (Hwang et al., 2016), as well as refining both datasets to select high quality training examples. Our work present two new systems, CombiNMT995, which is a result of matched sentences with a cosine similarity of 0.995 or less, and CombiNMT98, which, similarly, runs on a cosine similarity of 0.98 or less. By extending the human evaluation presented within the original paper, increasing both the number of annotators and the number of sentences annotated, with the intention of increasing the quality of the results, CombiNMT998 shows significant improvement over any of the Neural Text Simplification (NTS) systems from the original paper in terms of both the number of changes and the percentage of correct changes made.

2019

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Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table
Matthew Shardlow | Raheel Nawaz
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.

2018

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A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database
Matthew Shardlow | Nhung Nguyen | Gareth Owen | Claire O’Donovan | Andrew Leach | John McNaught | Steve Turner | Sophia Ananiadou
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
Luciano Gerber | Matthew Shardlow
Proceedings of the 12th International Workshop on Semantic Evaluation

We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset (attaining a macro f-score of 63.185 for English and 81.381 for Spanish), our approach did not perform as well on the test data. We describe our features and classi cation protocol, as well as initial experiments, concluding with a discussion of the discrepancy between our trial and test results.

2016

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NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features
Piotr Przybyła | Nhung T. H. Nguyen | Matthew Shardlow | Georgios Kontonatsios | Sophia Ananiadou
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2014

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Out in the Open: Finding and Categorising Errors in the Lexical Simplification Pipeline
Matthew Shardlow
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Lexical simplification is the task of automatically reducing the complexity of a text by identifying difficult words and replacing them with simpler alternatives. Whilst this is a valuable application of natural language generation, rudimentary lexical simplification systems suffer from a high error rate which often results in nonsensical, non-simple text. This paper seeks to characterise and quantify the errors which occur in a typical baseline lexical simplification system. We expose 6 distinct categories of error and propose a classification scheme for these. We also quantify these errors for a moderate size corpus, showing the magnitude of each error type. We find that for 183 identified simplification instances, only 19 (10.38%) result in a valid simplification, with the rest causing errors of varying gravity.

2013

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The CW Corpus: A New Resource for Evaluating the Identification of Complex Words
Matthew Shardlow
Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations

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A Comparison of Techniques to Automatically Identify Complex Words.
Matthew Shardlow
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop